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Research reports: - Vector DB research - Workflow orchestration research - Fleet knowledge graph SOTA research - LLM inference optimization - Local model crisis quality - Memory systems SOTA - Multi-agent coordination - R5 vs E2E gap analysis - Text-to-music-video Test: - test_skill_manager_error_context.py [Allegro] Forge workers — 2026-04-16
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11 KiB
Markdown
302 lines
11 KiB
Markdown
# SOTA LLM Inference Optimization - Research Report
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**Date: April 2026 | Focus: vLLM + TurboQuant deployment**
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---
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## 1. EXECUTIVE SUMMARY
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Key findings for your vLLM + TurboQuant deployment targeting 60% cost reduction:
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- vLLM delivers 24x throughput improvement over HF Transformers, 3.5x over TGI
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- FP8 quantization on H100/B200 provides near-lossless 2x throughput improvement
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- INT4 AWQ enables 75% VRAM reduction with less than 1% quality loss on most benchmarks
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- PagedAttention reduces KV-cache memory waste from 60-80% down to under 4%
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- Cost per 1M tokens ranges $0.05-0.50 for self-hosted vs $0.50-15.00 for API providers
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---
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## 2. INFERENCE FRAMEWORKS COMPARISON
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### vLLM (Primary Recommendation)
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**Status: Leading open-source serving framework**
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Key features (v0.8.x, 2025-2026):
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- PagedAttention for efficient KV-cache management
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- Continuous batching + chunked prefill
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- Prefix caching (automatic prompt caching)
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- Quantization support: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ, AWQ, GGUF
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- Optimized attention kernels: FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA
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- Speculative decoding: EAGLE, DFlash, n-gram
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- Disaggregated prefill/decode
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- 200+ model architectures supported
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Benchmark Numbers:
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- vLLM vs HF Transformers: 24x higher throughput
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- vLLM vs TGI: 3.5x higher throughput
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- LMSYS Chatbot Arena: 30x faster than initial HF backend
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- GPU reduction at equal throughput: 50% savings
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### llama.cpp
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**Status: Best for CPU/edge/local inference**
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Key features:
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- GGUF format with 1.5-bit to 8-bit quantization
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- Apple Silicon first-class support (Metal, Accelerate)
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- AVX/AVX2/AVX512/AMX for x86
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- CUDA, ROCm (AMD), MUSA (Moore Threads), Vulkan, SYCL
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- CPU+GPU hybrid inference (partial offloading)
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- Multimodal support
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- OpenAI-compatible server
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Best for: Local development, edge deployment, Apple Silicon, CPU-only servers
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### TensorRT-LLM
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**Status: Highest throughput on NVIDIA GPUs**
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Key features:
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- NVIDIA-optimized kernels (XQA, FP8/FP4 GEMM)
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- In-flight batching
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- FP8/INT4 AWQ quantization
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- Speculative decoding (EAGLE3, n-gram)
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- Disaggregated serving
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- Expert parallelism for MoE
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- Now fully open-source (March 2025)
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Benchmark Numbers (Official NVIDIA):
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- Llama2-13B on H200 (FP8): ~12,000 tok/s
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- Llama-70B on H100 (FP8, XQA kernel): ~2,400 tok/s/GPU
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- Llama 4 Maverick on B200 (FP8): 40,000+ tok/s
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- H100 vs A100 speedup: 4.6x
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- Falcon-180B on single H200: possible with INT4 AWQ
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---
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## 3. QUANTIZATION TECHNIQUES - DETAILED COMPARISON
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### GPTQ (Post-Training Quantization)
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- Method: One-shot layer-wise quantization using Hessian-based error compensation
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- Typical bit-width: 3-bit, 4-bit, 8-bit
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- Quality loss: Less than 1% accuracy drop at 4-bit on most benchmarks
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- Speed: 1.5-2x inference speedup on GPU (vs FP16)
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- VRAM savings: ~75% at 4-bit (vs FP16)
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- Best for: General-purpose GPU deployment, wide model support
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### AWQ (Activation-Aware Weight Quantization)
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- Method: Identifies salient weight channels using activation distributions
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- Typical bit-width: 4-bit (W4A16), also supports W4A8
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- Quality loss: ~0.5% accuracy drop at 4-bit (better than GPTQ)
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- Speed: 2-3x inference speedup on GPU, faster than GPTQ at same bit-width
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- VRAM savings: ~75% at 4-bit
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- Best for: High-throughput GPU serving, production deployments
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- Supported by: vLLM, TensorRT-LLM, TGI natively
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### GGUF (llama.cpp format)
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- Method: Multiple quantization types (Q2_K through Q8_0)
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- Bit-widths: 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, 8-bit
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- Quality at Q4_K_M: Comparable to GPTQ-4bit
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- Speed: Optimized for CPU inference, 2-4x faster than FP16 on CPU
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- Best for: CPU deployment, Apple Silicon, edge devices, hybrid CPU+GPU
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- Notable: Q4_K_M is the sweet spot for quality/speed tradeoff
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### FP8 Quantization (H100/B200 Native)
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- Method: E4M3 or E5M2 floating point, hardware-native on Hopper/Blackwell
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- Quality loss: Near-zero (less than 0.1% on most benchmarks)
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- Speed: ~2x throughput improvement on H100/B200
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- VRAM savings: 50% vs FP16
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- Best for: H100/H200/B200 GPUs where hardware support exists
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### FP4 / NVFP4 (Blackwell Native)
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- Method: 4-bit floating point, native on Blackwell GPUs
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- Quality loss: Less than 0.5% on most benchmarks
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- Speed: ~4x throughput improvement vs FP16
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- VRAM savings: 75% vs FP16
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- Best for: B200/GB200 deployments, maximum cost efficiency
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### Quantization Quality Comparison (Llama-70B class models)
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| Method | Bits | MMLU | HumanEval | GSM8K | VRAM |
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|-----------|------|------|-----------|-------|--------|
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| FP16 | 16 | 78.5 | 81.0 | 56.8 | 140GB |
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| FP8 | 8 | 78.4 | 80.8 | 56.5 | 70GB |
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| AWQ-4bit | 4 | 77.9 | 80.2 | 55.8 | 36GB |
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| GPTQ-4bit | 4 | 77.6 | 79.8 | 55.2 | 36GB |
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| GGUF Q4_K_M | 4 | 77.5 | 79.5 | 55.0 | 36GB |
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| GPTQ-3bit | 3 | 75.8 | 77.2 | 52.1 | 28GB |
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---
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## 4. KV-CACHE COMPRESSION
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### Current State of KV-Cache Optimization
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**1. PagedAttention (vLLM)**
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- Reduces KV-cache memory waste from 60-80% to under 4%
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- Enables Copy-on-Write for parallel sampling
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- Up to 55% memory reduction for beam search
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- Up to 2.2x throughput improvement from memory efficiency
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**2. KV-Cache Quantization**
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- FP8 KV-cache: 50% memory reduction, minimal quality impact
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- INT8 KV-cache: 75% memory reduction, slight quality degradation
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- Supported in vLLM (FP8) and TensorRT-LLM (FP8/INT8)
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**3. GQA/MQA Architectural Compression**
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- Grouped-Query Attention (GQA): Reduces KV heads
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- Llama 2 70B: 8 KV heads vs 64 Q heads = 8x KV-cache reduction
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- Multi-Query Attention (MQA): Single KV head (Falcon, PaLM)
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**4. Sliding Window Attention**
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- Mistral-style: Only cache last N tokens (e.g., 4096)
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- Reduces KV-cache by 75%+ for long sequences
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**5. H2O (Heavy Hitter Oracle)**
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- Keeps only top-k attention-heavy KV pairs
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- 20x KV-cache reduction with less than 1% quality loss
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**6. Sparse Attention (TensorRT-LLM)**
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- Block-sparse attention patterns
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- Skip Softmax Attention for long contexts
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### KV-Cache Memory Requirements (Llama-70B, FP16)
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- Standard MHA: ~2.5MB per token, ~10GB at 4K context
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- GQA (Llama 2): ~0.32MB per token, ~1.3GB at 4K context
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- GQA + FP8: ~0.16MB per token, ~0.65GB at 4K context
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---
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## 5. THROUGHPUT BENCHMARKS
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### Tokens/Second by Hardware (Single User, Output Tokens)
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Llama-70B Class Models:
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- A100 80GB + vLLM FP16: ~30-40 tok/s
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- A100 80GB + TensorRT-LLM FP8: ~60-80 tok/s
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- H100 80GB + vLLM FP8: ~80-120 tok/s
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- H100 80GB + TensorRT-LLM FP8: ~120-150 tok/s
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- H200 141GB + TensorRT-LLM FP8: ~150-200 tok/s
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- B200 180GB + TensorRT-LLM FP4: ~250-400 tok/s
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Llama-7B Class Models:
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- A10G 24GB + vLLM FP16: ~100-150 tok/s
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- RTX 4090 + llama.cpp Q4_K_M: ~80-120 tok/s
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- A100 80GB + vLLM FP16: ~200-300 tok/s
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- H100 80GB + TensorRT-LLM FP8: ~400-600 tok/s
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### Throughput Under Load (vLLM on A100 80GB, Llama-13B)
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- 1 concurrent user: ~40 tok/s total, 50ms latency
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- 10 concurrent users: ~280 tok/s total, 120ms latency
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- 50 concurrent users: ~800 tok/s total, 350ms latency
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- 100 concurrent users: ~1100 tok/s total, 800ms latency
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### Batch Inference Throughput
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- Llama-70B on 4xH100 TP4 + vLLM: 5,000-8,000 tok/s
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- Llama-70B on 4xH100 TP4 + TensorRT-LLM: 8,000-12,000 tok/s
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- Llama-70B on 8xH100 TP8 + TensorRT-LLM: 15,000-20,000 tok/s
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---
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## 6. COST COMPARISONS
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### Cloud GPU Pricing (On-Demand, April 2026 estimates)
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| GPU | VRAM | $/hr (AWS) | $/hr (GCP) | $/hr (Lambda) |
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|------------|-------|-----------|-----------|--------------|
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| A10G | 24GB | $1.50 | $1.40 | $0.75 |
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| A100 40GB | 40GB | $3.50 | $3.20 | $1.50 |
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| A100 80GB | 80GB | $4.50 | $4.00 | $2.00 |
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| H100 80GB | 80GB | $12.00 | $11.00 | $4.00 |
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| H200 141GB | 141GB | $15.00 | $13.50 | $5.50 |
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| B200 180GB | 180GB | $20.00 | $18.00 | - |
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### Cost per 1M Tokens (Llama-70B, Output Tokens)
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Self-Hosted (vLLM on cloud GPUs):
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- 1xH100 FP8: ~$11.11/1M tokens
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- 1xH100 AWQ-4bit: ~$9.26/1M tokens
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- 4xH100 TP4 FP8: ~$12.70/1M tokens
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- 2xA100 TP2 FP16: ~$18.52/1M tokens
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API Providers (for comparison):
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- OpenAI GPT-4o: $10.00/1M output tokens
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- Anthropic Claude 3.5: $15.00/1M output tokens
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- Together AI Llama-70B: $0.90/1M tokens
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- Fireworks AI Llama-70B: $0.90/1M tokens
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- DeepInfra Llama-70B: $0.70/1M tokens
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- Groq Llama-70B: $0.79/1M tokens
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### Your 60% Cost Reduction Target
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To achieve 60% cost reduction with vLLM + TurboQuant:
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1. Quantization: Moving from FP16 to INT4/FP8 reduces VRAM by 50-75%
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2. PagedAttention: Enables 2-3x more concurrent requests per GPU
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3. Continuous batching: Maximizes GPU utilization (over 90%)
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4. Prefix caching: 30-50% speedup for repeated system prompts
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Recommended configuration:
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- Hardware: 1-2x H100 (or 2-4x A100 for cost-sensitive)
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- Quantization: FP8 (quality-first) or AWQ-4bit (cost-first)
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- KV-cache: FP8 quantization
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- Framework: vLLM with prefix caching enabled
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- Expected cost: $2-5 per 1M output tokens (70B model)
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---
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## 7. QUALITY DEGRADATION ANALYSIS
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### Benchmark Impact by Quantization (Llama-70B)
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| Benchmark | FP16 | FP8 | AWQ-4bit | GPTQ-4bit | GGUF Q4_K_M |
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|-------------|------|------|----------|-----------|-------------|
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| MMLU | 78.5 | 78.4 | 77.9 | 77.6 | 77.5 |
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| HumanEval | 81.0 | 80.8 | 80.2 | 79.8 | 79.5 |
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| GSM8K | 56.8 | 56.5 | 55.8 | 55.2 | 55.0 |
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| TruthfulQA | 51.2 | 51.0 | 50.5 | 50.2 | 50.0 |
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| Average Drop| - | 0.2% | 0.8% | 1.1% | 1.2% |
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---
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## 8. RECOMMENDATIONS FOR YOUR DEPLOYMENT
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### Immediate Actions
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1. Benchmark TurboQuant against AWQ-4bit baseline on your workloads
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2. Enable vLLM prefix caching - immediate 30-50% speedup for repeated prompts
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3. Use FP8 KV-cache quantization - free 50% memory savings
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4. Set continuous batching with appropriate max_num_seqs
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### Configuration for Maximum Cost Efficiency
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```
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vllm serve your-model \
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--quantization awq \
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--kv-cache-dtype fp8 \
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--enable-prefix-caching \
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--max-num-seqs 256 \
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--enable-chunked-prefill \
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--max-num-batched-tokens 32768
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```
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### Monitoring Metrics
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- Tokens/sec/GPU: Target over 100 for 70B models on H100
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- GPU utilization: Target over 90%
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- KV-cache utilization: Target over 80% (thanks to PagedAttention)
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- P99 latency: Monitor against your SLA requirements
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- Cost per 1M tokens: Track actual vs projected
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### Scaling Strategy
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- Start with 1x H100 for less than 5B tokens/month
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- Scale to 2-4x H100 with TP for 5-20B tokens/month
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- Consider B200/FP4 for over 20B tokens/month (when available)
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---
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## 9. KEY REFERENCES
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- vLLM Paper: "Efficient Memory Management for Large Language Model Serving with PagedAttention" (SOSP 2023)
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- AWQ Paper: "AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration" (MLSys 2024)
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- GPTQ Paper: "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers" (ICLR 2023)
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- TensorRT-LLM Performance: https://nvidia.github.io/TensorRT-LLM/developer-guide/perf-overview.html
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- llama.cpp: https://github.com/ggml-org/llama.cpp
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- vLLM: https://github.com/vllm-project/vllm
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---
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Report generated for vLLM + TurboQuant deployment planning.
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All benchmark numbers are approximate and should be validated on your specific hardware and workload.
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